Best Practices for Writing Efficient SQL Queries

Introduction: Efficient SQL Is Not “Fancy SQL”

Efficient SQL is not about writing complex queries that look impressive. It is about writing queries that return the right result quickly, reliably, and consistently even when the table grows from thousands of rows to millions.
If your query is slow, business decisions become slow. Reports feel unreliable. Dashboards lag. Users lose trust. And in interviews, slow SQL is often treated as weak fundamentals.
This guide gives you best practices you can apply immediately whether you work in reporting, analytics, or application development.

1) Start With the Goal: Return Only What You Need

The fastest query is the query that does not do unnecessary work.

**Use selective columns, not SELECT ***
● SELECT * pulls all columns, even unused ones.
● It increases I/O, memory usage, and network transfer.
● It can break downstream code when schema changes.

Best practice: Select only required columns.

Filter early
Apply WHERE conditions as early as possible so the database reads fewer rows.

2) Understand Data Volume Before Optimizing Anything

Two queries can look similar but behave wildly differently depending on data distribution.
Before tuning:
● Check table size
● Check how many rows match filters
● Check whether values are skewed (example: 90% rows have the same status)

Best practice: Optimization without knowing row counts is guessing.

3) Use Indexes Strategically (Not Everywhere)

Indexes speed up reads but slow down writes and take storage.

Where indexes help most
● Columns used in WHERE filters frequently
● Join keys (ON columns)
● Columns used in ORDER BY or GROUP BY (in some cases)

Avoid indexing low-cardinality columns blindly
Columns like gender, is_active, status often have few distinct values.
Indexes may not help unless combined with other columns or used selectively.

Best practice: Index based on real query patterns, not assumptions.

4) Write Joins That Match the Real Relationship

Joins are one of the biggest reasons queries slow down.

Always join on indexed, meaningful keys
Joining on non-key columns increases comparison cost and can create duplicates.

Pick the correct join type
● INNER JOIN when you only need matched rows
● LEFT JOIN when you must keep all rows from the left table

Best practice: Avoid using LEFT JOIN by default use it only when required.

Watch for accidental many-to-many joins
If both tables have duplicates for the join key, row multiplication happens.
This explodes data size and slows everything.

Best practice: Confirm key uniqueness before joining.

5) Avoid Functions on Filter Columns

A very common performance killer is applying functions to columns in the WHERE clause.

Example patterns that often hurt performance:
● WHERE YEAR(order_date) = 2025
● WHERE LOWER(email) = '[email protected]'

Why it hurts:
● The database may not use indexes efficiently because the column is transformed.

Best practice: Transform the constant, not the column, or filter with ranges.

For dates:
Use range filtering like:
● order_date >= '2025-01-01' AND order_date < '2026-01-01'

6) Be Careful With DISTINCT

DISTINCT is often used as a “quick fix” when joins create duplicates.
But it forces extra sorting or hashing.

Best practice: Fix the join logic instead of using DISTINCT to hide problems.

Use DISTINCT only when:
● You truly need unique results, and
● You understand why duplicates appear

7) Prefer EXISTS Over IN in Many Real Cases

When checking existence, EXISTS often performs well because it can stop early.
● EXISTS checks for presence and can short-circuit.
● IN may build a full set depending on the optimizer and engine.

Best practice: For existence checks on large datasets, start with EXISTS.

8) Use Aggregations Carefully

GROUP BY and large aggregations can be expensive.

Reduce rows before aggregating
Filter and join only necessary data before grouping.

Group only needed columns
More columns in GROUP BY means more groups and more memory.

Best practice: Aggregate on the smallest dataset that still answers the question.

9) Avoid Correlated Subqueries When a Join Can Do Better

A correlated subquery runs once per row in many cases this can be slow.

Best practice: Rewrite correlated subqueries into joins or window functions when appropriate.

10) Use Window Functions for Analytics Without Over-Grouping

Window functions like ROW_NUMBER(), RANK(), SUM() OVER() often reduce complexity.
They help you:
● Find top-N per group
● Compute running totals
● Compare row-to-row values

Best practice: Prefer window functions over complex self-joins where suitable.

11) Paginate Correctly for Large Results

If your app or report shows results page by page, don’t fetch everything.

Best practice:
● Use pagination with stable ordering
● Prefer keyset pagination for very large datasets when possible

12) Use the Execution Plan Like a Doctor Uses a Scan

If you want real performance improvements, the execution plan is your truth.
Look for:
● Full table scans when you expected index usage
● Large sorts
● Expensive hash joins
● High estimated vs actual row differences

Best practice: Tune based on what the database is doing not what you think it is doing.

13) Keep SQL Readable (Because Readable SQL Gets Optimized Faster)

A query that is easy to understand is easier to tune and less likely to contain logical errors.

Best practices for readability:
● Use meaningful aliases
● Format joins and filters cleanly
● Break complex queries into CTEs where helpful
● Avoid unnecessary nesting

Performance and readability often improve together.

14) Reduce Round Trips in Reporting

In reporting systems, multiple small queries can be slower than one well-designed query.

Best practice: Combine related logic when it reduces repeated scanning, but don’t create a monster query that nobody can maintain.

15) Measure Performance Properly

A query can appear “fast” on a small dev dataset and fail in production.
Always test with:
● Realistic data volumes
● Realistic filters
● Cold cache vs warm cache scenarios (if relevant)

Best practice: What matters is production behavior, not local behavior.

Quick Checklist: Efficient SQL Habits

● Select only needed columns
● Filter early with WHERE
● Use correct join types
● Confirm key uniqueness before joining
● Avoid functions on filter columns
● Use indexes based on query patterns
● Use EXISTS for existence checks
● Reduce data before grouping
● Use window functions for analytics
● Read execution plans for real tuning

FAQ

1) What is the biggest reason SQL queries become slow?
Most often: scanning too many rows due to missing filters, poor indexing, or inefficient joins.

2) Is indexing always the solution?
No. Indexing helps reads but can slow inserts/updates and increase storage. Index based on real use.

3) Why is SELECT * bad?
It reads unnecessary columns, increases I/O, and can break reporting logic when new columns are added.

4) How do I know if my query uses an index?
Check the execution plan. If you see an index seek/scan (depending on engine), it indicates index usage.

5) Should I always use CTEs for performance?-
CTEs improve readability. Performance depends on how the optimizer handles them. Use them for clarity first, then tune. To master these optimization concepts and advanced SQL, explore our comprehensive Data Analytics & Business Analytics course.